A structural shift is occurring as artificial intelligence transitions from being a tool for generating output to one that executes tasks across IT environments, significantly increasing the demand for robust governance and infrastructure controls. This mechanism is illustrated by the rapid integration of agentic automation into operational platforms, with vendors such as Kyndryl (Agentic Service Management) and SolarWinds (SW1) positioning their AI systems as operational teammates capable of autonomous action. Analysts from firms like Omnia and AvePoint highlight that the product focus is no longer the agent or AI capability itself, but the enforcement layer—encompassing identity management, permissions, logging, quota enforcement, tenant boundaries, and approval workflows.

A consequential development is the increased operational burden on networks, as agentic automation increases background and automated traffic. According to Imperial's Bad Bot report, automated traffic now exceeds 51% of all internet activity. Analyst firm Omnia and Lumen CEO Kate Johnson stress that the capacity of underlying networks, and not just compute resources, is becoming a hard constraint for scaling AI-driven operations. For MSPs, this manifests as tangible increases in bandwidth contention, authentication events, and noise in security tooling, leading to resource constraints and increased pressure on triage and incident response.

Complementary developments reinforce this shift. Enable is rolling out direct AI operational integration in N-Central and Insight through a custom context protocol, while OpenAI is updating its agents' SDK to include sandboxing and distribution harnesses for stricter boundaries. The New Stack underscores NIST’s recommendation for layered controls, least privilege, network segmentation, and tamper-resistant, replayable logging to contain the risks associated with agentic automation. Research cited by the AI Journal finds that governance and compliance, rather than technical skills, are currently the top barriers to reliable AI adoption among MSPs, driven by the complexity of multi-tenant environments and the requirement to prove control and recoverability.

For MSPs and IT providers, these shifts introduce direct operational and contractual risks. Relying on default vendor models without explicit policy ownership or proof-of-execution effectively transfers liability without control. Practical considerations now require MSPs to define approval models, enforce least privilege, audit agent actions, establish recovery playbooks, forecast network and compute demand, and clarify quotas and overage terms in service contracts. Unbounded and unaudited automation is becoming a commercially unacceptable risk, comparable to operating critical systems without proper backups.

00:00 AI Tax: Networks
04:35 Scaffolding Over Models
07:45 Agents Eat Margins
10:05 Why Do We Care?

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